Systems and methods for treatment planning based on plaque progression and regression curves

ABSTRACT

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient&#39;s vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient&#39;s vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient&#39;s vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.62/033,446 filed Aug. 5, 2014, the entire disclosure of which is herebyincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure relate generally todisease assessment, treatment planning, and related methods. Morespecifically, particular embodiments of the present disclosure relate tosystems and methods for treatment planning based on coronary plaqueprogression/regression curves.

BACKGROUND

Coronary artery disease is a common ailment that affects millions ofpeople. Coronary artery disease may cause the blood vessels providingblood to the heart to develop lesions, such as a stenosis (abnormalnarrowing of a blood vessel). As a result, blood flow to the heart maybe restricted. A patient suffering from coronary artery disease mayexperience chest pain, referred to as chronic stable angina duringphysical exertion or unstable angina when the patient is at rest. A moresevere manifestation of disease may lead to myocardial infarction, orheart attack. Significant strides have been made in the treatment ofcoronary artery disease including both medical therapy (e.g. statins) orsurgical alternatives (e.g., percutaneous coronary intervention (PCI)and coronary artery bypass graft surgery (CABG)). For example, PCI andCABG may be implemented where lesions are detected. However, some arteryblockage may not be functionally significant. In other words, a blockagemay not require surgical intervention if the blockage does notsignificantly obstruct flow and interfere with oxygen delivery to heartmuscle.

One important hemodynamic measure used in the diagnosis of functionallysignificant lesions is fractional flow reserve (“FFR”). FFR may quantifythe ratio of pressure at a distal location in the coronary artery to theaortic pressure. This ratio is seen as indicative of the likelihood thata stenosis is functionally significant. Risky, expensive, and invasivecatheterization of the coronary artery is traditionally used to measureFFR. However, recent advances may show that FFR may be calculatednon-invasively using blood flow modeling and coronary computedtomography scans. In other words, FFR measurements or predictions may bemore readily available with the recent developments in non-invasiveacquisition of FFR.

However, the genesis and progression of coronary disease involves acomplex combination of chemical, biological, and mechanical pathwaysacross molecular, cellular, and tissue scales that is yet to be fullyunderstood. Thus, a desire still exists to provide more accurate datarelating to coronary lesions, e.g., size, shape, location, functionalsignificance (e.g., whether the lesion impacts blood flow), etc. Forexample, understanding the geometry of vascular disease may involvestudying the growth and remodeling of plaque (e.g., in coronary vasculardisease).

In some cases, plaque characteristics may influence growth andremodeling of plaques. For example, calcified plaques may be typicallystable and may not significantly remodel. Further, calcified plaques maybe less receptive to medical therapy, for instance, statin treatment. Incontrast, fatty and fibro-fatty plaques with a lipid core may have ahigher remodeling index, and may be more receptive to medical therapyand lifestyle changes (e.g. exercise). In addition to plaquecharacteristics, factors including hemodynamic forces, plaquecomposition, plaque location, intramural stress, etc. may alsocontribute to the ability of plaque to remodel.

Thus, a desire exists to better understand the mechanism of how plaquegeometry impacts the functional significance of disease (e.g., FFR) in apatient's vasculature. By extension, a desire exists to improve anunderstanding of pathogenesis and disease progression or regression. Animproved understanding of the relationship between plaque geometry andpathogenesis may advance treatment planning, decreasing the frequency ofunnecessary invasive treatment and ensuring selection of treatmenteffective for specific patient and disease plaque characteristics.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for treatment planning based on plaque progressionand regression curves. For example, such systems and methods may includeidentifying which coronary artery plaques are sensitive to progression,stability or regression and the consequent effect on blood flow andpressure in the effected artery.

One method includes: receiving patient-specific data regarding ageometry of the patient's vasculature; creating an anatomic modelrepresenting at least a portion of a location of disease in thepatient's vasculature based on the received patient-specific data;identifying one or more changes in geometry of the anatomic model basedon a modeled progression or regression of disease at the location;calculating one or more values of a blood flow characteristic within thepatient's vasculature using a computational model based on theidentified one or more changes in geometry of the anatomic model; andgenerating an electronic graphical display of a relationship between theone or more values of the calculated blood flow characteristic and theidentified one or more changes in geometry of the anatomic model.

In accordance with another embodiment, a system for evaluating a patientwith vascular disease comprises: a data storage device storinginstructions for evaluating a patient with vascular disease; and aprocessor configured for: receiving patient-specific data regarding ageometry of the patient's vasculature; creating an anatomic modelrepresenting at least a portion of a location of disease in thepatient's vasculature based on the received patient-specific data;identifying one or more changes in geometry of the anatomic model basedon a modeled progression or regression of disease at the location;calculating one or more values of a blood flow characteristic within thepatient's vasculature using a computational model based on theidentified one or more changes in geometry of the anatomic model; andgenerating an electronic graphical display of a relationship between theone or more values of the calculated blood flow characteristic and theidentified one or more changes in geometry of the anatomic model.

In accordance with another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for performing a method ofevaluating a patient with vascular disease, the method comprising:receiving patient-specific data regarding a geometry of the patient'svasculature; creating an anatomic model representing at least a portionof a location of disease in the patient's vasculature based on thereceived patient-specific data; identifying one or more changes ingeometry of the anatomic model based on a modeled progression orregression of disease at the location; calculating one or more values ofa blood flow characteristic within the patient's vasculature using acomputational model based on the identified one or more changes ingeometry of the anatomic model; and generating an electronic graphicaldisplay of a relationship between the one or more values of thecalculated blood flow characteristic and the identified one or morechanges in geometry of the anatomic model.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network fortreatment planning based on plaque progression and regression curves,according to an exemplary embodiment of the present disclosure.

FIG. 2 is a block diagram of an exemplary method of treatment planningbased on lesion type and hemodynamic sensitivity, according to anexemplary embodiment of the present disclosure.

FIG. 3A is a block diagram of an exemplary method of treatment planningbased on progression/regression curves, given specific patientcharacteristics, according to an exemplary embodiment of the presentdisclosure.

FIG. 3B is a block diagram of an exemplary method of creating ananatomical model including diseased regions, according to an exemplaryembodiment of the present disclosure.

FIG. 3C is a block diagram of an exemplary method of analyzing theimpact of disease progression and regression on hemodynamics, accordingto an exemplary embodiment of the present disclosure.

FIG. 4A is a block diagram of an exemplary method of a specificembodiment for treatment planning based on plaque progression/regressioncurves for coronary artery disease, according to an exemplary embodimentof the present disclosure.

FIG. 4B is a block diagram of an exemplary method for finding a diseasedregion of anatomy, according to an exemplary embodiment of the presentdisclosure.

FIG. 4C is a block diagram of an exemplary method for characterizing adisease type, according to an exemplary embodiment of the presentdisclosure.

FIG. 5 shows an exemplary display including plaqueprogression/regression curves for a soft lipid-rich plaque, according toan embodiment of the present disclosure.

FIG. 6 shows an exemplary display including plaqueprogression/regression curves for a soft lipid-rich plaque, according toan embodiment of the present disclosure.

FIG. 7 shows an exemplary display of FFR_(CT) variation due toremodeling of a lesion, according to an embodiment of the presentdisclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

As described above, the functional significance of changes in arterialplaque geometry may be assessed using fractional flow reserve (FFR). FFRis recognized as an important hemodynamic measure for diagnosing thefunctional significance of lesions. FFR may quantify the ratio ofpressure at a distal location in the coronary artery to the aorticpressure. The impact of plaque geometry on FFR (and thereby, blood flow)in a patient-specific coronary vasculature has not been well understoodor quantified. Therefore, a desire exists to improve understanding ofhow atherosclerotic disease progression or regression alters plaquegeometry and impacts blood flow.

Thus, the present disclosure is directed to techniques for automaticallyevaluating and electronically displaying the relationship betweenchanges in plaque geometry and the functional significance of a lesionassociated with the plaque geometry. In other words, the presentdisclosure is directed to systems and methods for determining how thegrowth/shrinkage of plaque geometry affects hemodynamics. In oneembodiment, the present disclosure describes a system and method tounderstand the impact of lumen geometry on hemodynamic diseaseindicators. Predictive modeling of functional significance in responseto changes in plaque geometry may help treatment planning for anindividual. Specifically, the understanding of the impact may becombined with plaque characteristics to arrive at an optimal treatmentstrategy (e.g., as described in further detail in FIG. 2).

In one embodiment, the impact of lumen geometry on disease progressionfor an individual may be measured based on predictive modeling of plaquegrowth/shrinkage. Plaque progression/regression and tissue remodelingmay be predicted by either (i) using computational modeling of theadaptive response of arterial walls in response to altered pressure orflow (from homeostatic conditions) or biological factors (e.g.,hypertension, cholesterol level, activity level, smoking, etc.) or (ii)employing machine learning to predict plaque progression andvulnerability using features (e.g., plaque location, presence ofproximal/distal disease, hypertension, cholesterol, activity level,smoking, etc.). Exemplary methods for machine learning approaches aredisclosed, for example, in U.S. Nonprovisional application Ser. No.14/011,151, filed Aug. 27, 2013, entitled “Systems and Methods forPredicting Location, Onset, and/or Change of Coronary Lesions,” which ishereby incorporated by reference herein in its entirety. In some cases,FFR values calculated using these machine learning techniques may bereferred to as FFRML.

A combination of plaque remodeling index, along with variability in FFRassessed using a statistical method, may be used to calculate the riskof plaque progression. In one embodiment, the variability of FFR, inresponse to the plaque remodeling index, may be recognized ashemodynamic sensitivity. Sensitivity analysis may also be used tocharacterize how FFR responds to changes in radius or minimum lumendiameter (MLD), for example, in the form of FFR_(CT) vs. MLD curves (asshown in FIGS. 6 and 7). In one embodiment, the sensitivity curve may becalculated by performing one or both of: three-dimensional (3-D)simulations or machine learning analysis on candidate geometriesidentified by a stochastic collocation algorithm. Alternatively, aMonte-Carlo algorithm or a similar sampling of uniformly spacedgeometries may be used to calculate the sensitivity curve. The slope ofthe FFR_(CT) vs. MLD curve may be used to calculate the effect of plaqueprogression (e.g., where the slope is negative) and regression (e.g.,where the slope is positive).

If plaque progression has low risk or FFR_(CT) has low sensitivity togeometry, FFR_(CT) sensitivity due to plaque progression may be low. Amachine learning approach may be trained to predict sensitivity withrespect to plaque progression by combining the features to predictplaque remodeling above, along with features to predict FFRML (e.g.,lumen area, hemodynamic factors (such as net downstream resistances),etc.). An exemplary method for estimating FFR_(CT) sensitivity isdisclosed, for example, in U.S. Nonprovisional application Ser. No.13/864,996, filed Apr. 17, 2013, entitled “Method and System forSensitivity Analysis in Modeling Blood Flow Characteristics,” which ishereby incorporated herein by reference herein in its entirety.

In one embodiment, outputs may be quantified and visualized by coloringlesions based on the impact of a lesion on FFR_(CT). The outputs mayinclude one or a combination of (a) patient-specific geometry, whereeach lesion may be color-coded by its impact on FFR_(CT), (b) a newFFR_(CT) map in remodeled geometry, one for each lesion, (c) acharacteristic progression/regression curve for each lesion, and/or (d)a quantified and output risk score based on a combination of remodelingindex and FFR_(CT) sensitivity to plaque geometry. Such visual outputsmay be used to stratify highly sensitive lesions and assess the impactof medical therapy (e.g., for plaques that are sensitive to regression)or more frequent follow-ups (e.g., for patients who have lesions thathave a negative functional significance (e.g., negative FFR_(CT)diagnosis) but are sensitive to progression). A desire thus exists todetermine such information, as it may be useful in predictivesimulations of how remodeling of plaque morphology impacts FFR_(CT).

The present disclosure also describes systems and methods for predictinghow progression and regression of lesions affect FFR_(CT), using aclassification system. For example, a taxonomy chart may be created fortreatment options (e.g., statin therapy, stenting, etc.) or lifestylechanges based on the type of lesion, baseline FFR_(CT), and a calculatedprogression/regression curve. Placement on the taxonomy chart may bebased on (a) the propensity of a lesion to remodel (e.g., soft plaques)and (b) the extent to which FFR_(CT) is impacted when the lesionremodels. The present disclosure may provide: (i) a prediction of howprogression/regression of disease (e.g., lesions) effects FFR_(CT) basedon disease type, (ii) a selection of treatment options based onprogression/regression of lesions, and/or (iii) a quantified and outputrisk score based on a combination of remodeling index and FFR_(CT)sensitivity to plaque geometry.

One embodiment for determining such information may include a broadtaxonomy using a combination of (i) lesion type (e.g.,calcified/fibrotic or lipid rich) and (ii) classification of lesions ashemodynamically sensitive or hemodynamically insensitive. Lipid-richlesions that may be hemodynamically sensitive may be candidates foreither medical therapy or PCI. Calcified lesions, independent ofhemodynamic sensitivity, may be stable, whereas lipid-rich lesions thatmay be hemodynamically insensitive may be indeterminate. As will bedescribed in more detail below, FIG. 2 includes an exemplary taxonomyfor treatment planning based on the impact of plaque characteristics andremodeling on hemodynamics.

Overall, predictive modeling of plaque growth/shrinkage, as well as theinteraction between plaque geometry and functional significance of thelesion may help in treatment planning of the patient, including makingdecisions on, for example, (i) whether aggressive medical therapy isoptimal for the patient, (ii) time intervals at which a follow-upimaging study may preferentially be performed for the patient, and/or(iii) whether stenting and/or bypass grafting are suitable treatmentoptions for a patient. The present disclosure is directed at quantifyingthe relation between plaque geometry and functional significance byplotting FFR_(CT) at candidate measurement locations against plaqueradius for each identified plaque (+/−25% MLD). Such plots may helpassess the cost and benefits for surgical treatment options, medicaltherapy, and/or continuous monitoring.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system 100 and network for treatment planning based on plaqueprogression/regression curves, according to an exemplary embodiment.Specifically, FIG. 1 depicts a plurality of physicians 102 and thirdparty providers 104, any of whom may be connected to an electronicnetwork 101, such as the Internet, through one or more computers,servers, and/or handheld mobile devices. Physicians 102 and/or thirdparty providers 104 may create or otherwise obtain images of one or morepatients' anatomy. The physicians 102 and/or third party providers 104may also obtain any combination of patient-specific information, such asage, medical history, blood pressure, blood viscosity, etc. Physicians102 and/or third party providers 104 may transmit the anatomical imagesand/or patient-specific information to server systems 106 over theelectronic network 101. Server systems 106 may include storage devicesfor storing images and data received from physicians 102 and/or thirdparty providers 104. Server systems 106 may also include processingdevices for processing images and data stored in the storage devices.

FIG. 2 is a block diagram of an exemplary schematic of a method 200 oftreatment planning based on lesion type and hemodynamic sensitivity,according to an exemplary embodiment. In one embodiment, method 200includes a sequence and/or a taxonomy for classifying plaques detectedin an individual's vasculature. In classifying the plaques, method 200may guide treatment planning toward treatments suitable for the plaquecharacteristics associated with the classification.

In one embodiment, method 200 may include step 201 of identifying alesion type (e.g., for a lesion detected in a patient's anatomy from CTor other digital imaging received at server systems 106). In oneembodiment, method 200 may distinguish between lesion types broadly, aseither calcified/fibrotic plaque 203 or soft plaque 205. Calcified orfibrotic plaques may be predominantly stable, so assessments may focuson whether the calcified or fibrotic plaques are candidates forstenting. Such assessments may include evaluating the extent to whichthe calcified or fibrotic plaques limit flow, and making a determinationregarding stenting based on the level of flow blockage caused by theplaque.

If a patient lesion is identified as being comprised of calcified orfibrotic plaque 203, method 200 may proceed to step 207 may includecomputing variation of FFR_(CT) in relation to plaque progression. Asdiscussed above, FFR describes the ratio of pressures distal to a lesionand proximal to a lesion (e.g., FFR=P_(distal)/P_(proximal)). In oneembodiment, P_(proximal) may be measured in the aorta. Large pressuredrops may be indicative of significant stenosis, meaning, functionallysignificant lesions may be associated with low FFR values. High FFRvalues (e.g., values closer to FFR=1) may indicate non-significantstenosis. For example, some embodiments may include an FFR thresholdvalue of 0.8, where FFR>0.8 may indicate a negative diagnosis, orinsignificant stenosis. By contrast, if FFR 0.8, an associated lesionmay receive a positive diagnosis, meaning significant stenosis.

Step 207 of computing FFR_(CT) may be used in subsequent step 209, whichmay include determining if a lesion of calcified or fibrotic plaque isflow limiting. In one embodiment, a lesion may be identified as flowlimiting if it is classified as having an FFR value of less than orequal to a predetermined threshold, e.g., FFR 0.8, and non-flow-limitingif it is classified as having an FFR value greater than a predeterminedthreshold, e.g., FFR>0.8. If the lesion is, in fact, flow limiting, thelesion may receive a “positive” diagnosis, meaning that significantstenosis exists at the lesion site. In such a situation, method 200 mayproceed to step 211, which may include identifying the patient lesion asa candidate for stenting and/or bypass grafting. Since calcified plaquesare less likely to regress, treatments recommended at step 211 may tendto include stenting or bypass grafts, rather than, for example, statintherapy. Alternately, step 209 may return an assessment that the patientlesion is not flow-limiting. In such a case, method 200 may proceed tostep 213, which may involve recommending standard medical therapy toreduce risk factors for disease and/or recommending merely following-upperiodically.

Where a patient lesion is identified as being a lipid-rich lesion ofsoft plaque 205, step 201 may be followed by step 215. Step 215 mayinclude computing how FFR_(CT) varies due to plaque progression orregression by first determining whether an FFR_(CT) value calculated inrelation to the plaque is negative or positive (i.e., respectively,above or below a predetermined threshold). Since soft plaque may be morehemodynamically sensitive than calcified or fibrotic plaque, step 215may include an observation of FFR_(CT) response to plaque regression. Bycontrast, step 207 may focus on only plaque progression, since calcifiedplaque may have a lower likelihood of regression.

Where step 215 determines that a lesion is not significant (i.e.,FFR_(CT) is negative), step 215 may be followed by step 217, which mayinclude determining whether the lesion becomes significant if plaqueprogresses by, for example, 20%. For instance, step 217 may make thisdetermination from a plaque progression curve of FFR_(CT) against plaquegeometry. If the lesion becomes significant from plaque progression pasta given point (step 217, “Yes”), then step 219 may include recommendingthat the lesion be monitored. For example, treatment may not yet benecessary, but step 219 may include recommending frequent follow-ups andstarting medical therapy if the lesion becomes more severe. If thelesion does not become significant, even with plaque progression (step217, “No”), then step 221 may include determining that standard medicaltherapy is warranted to reduce risk factors for atherosclerosis.

Where step 215 involves determining that the lesion is, in fact,significant (i.e., FFR_(CT) is diagnosed as positive, then step 215 maybe followed by step 223, which may include determining whether thelesion becomes no longer functionally significant, should the plaqueregress a given amount (e.g., 20% regression). If plaque regressionchanges the functional significance of the lesion (step 223, “Yes”),then step 223 may be followed by step 225, which may includerecommending a treatment including aggressive medical therapy. Forexample, step 225 may identify a patient as a candidate for aggressivestatin therapy and/or lifestyle changes (e.g., exercise). If plaqueregression is modeled as not changing a functional significance of thelesion (step 223, “No”), then step 223 may be followed by step 227,which may include determining whether the lesion is flow-limiting. Ifthe lesion is not identified as flow-limiting, then step 227 may befollowed by step 221, including determining that treatment or action maybe recommended for the lesion. If the lesion is flow-limiting, however,step 227 may be followed by step 229, where the lesion may be identifiedas a candidate for stenting and/or bypass grafting.

FIG. 3A is a block diagram of an exemplary method 300 of treatmentplanning based on progression/regression curves in light of specificpatient characteristics, according to an exemplary embodiment. Themethod of FIG. 3A may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 101.

In one embodiment, step 301 may include determining whether ananatomical model and/or disease information is available. Where ananatomical model and information regarding disease type or location(s)of disease for the anatomical model is not available, steps may be takento acquire such information. FIG. 3B describes an embodiment for modeland/or information acquisition.

In one embodiment, step 303 may include characterizing a disease typefrom the anatomical model. In some embodiments, the characterization mayinclude parameters (e.g., anatomical parameters, including changes invessel radius), such that portion(s) of a patient anatomical model thatmatch the characterization may be identified as diseased. For example,step 303 may include characterizing disease type based on a CT scan(e.g., accessed from step 301) and locations identified as diseased(e.g., available from step 301 or from method 320 of FIG. 3B). In someinstances, step 303 may include characterizing disease type based onanatomy, composition of plaque, or a combination thereof. Determiningdisease type may further involve accounting for a number of factors,including location of disease, thickness of wall, material properties ofthe wall, metabolic activity of constituents in the wall, etc. In someembodiments, disease type may be characterized using a quantitativemetric comprising combinations of the factors above. In one embodiment,step 303 may include creating the disease characterization. In otherembodiments, step 303 may include receiving a characterization of adisease.

In one embodiment, step 305 may include predicting plaquegrowth/regression. In other words, whereas step 303 may includedetecting disease, and more specifically, a present state of diseasebased on the patient anatomical model (e.g., from step 301), step 305may involve forming an understanding of future states of the disease.Treatment planning may be based on predictions from an understanding offuture states, as developed in step 305.

For example, step 305 may include determining, from the disease type andpatient information, a prognosis for adverse cardiac events and plaquevulnerability. In one embodiment, step 305 may include predicting plaquegrowth using image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, structural mechanics analysis, orany combination of these analyses of the anatomical image data.Alternatively or in addition, step 305 may employ a machine learningapproach to compute plaque growth. Predicting plaque growth may includeprognosis for adverse cardiac events and plaque vulnerability. Anexemplary method for predicting plaque vulnerability is disclosed, forexample, in U.S. Nonprovisional application Ser. No. 14/254,481, filedApr. 16, 2014, entitled “Systems and Methods for Predicting CoronaryPlaque Vulnerability from Patient-Specific Anatomic Image Data,” whichis hereby incorporated by reference herein in its entirety.

In one embodiment, step 307 may include an analysis of the functionalsignificance of possible plaque growth/regression (e.g., from step 305).For example, step 307 may include determining hemodynamic response tothe plaque growth/regression in order to assess functional significanceof detected disease. Determining hemodynamic response may includeevaluating the impact that plaque growth/regression may have onhemodynamics within respective portions of the blood vessels. FIG. 3Cincludes further detail regarding assessing the hemodynamic impact ofdisease progression and regression.

Step 309 may include determining an output. For example, step 309 mayinclude quantifying and outputting disease risk. In some situations,step 309 may include post-processing output. The quantifying andpost-processing of step 309 may include selecting an output (e.g., anoutput display) out of several possibly displays. Step 309 may furtherinclude creating renderings of displays or adjusting displays inresponse to user input or interaction. In one embodiment, step 309 mayinclude writing all, or a combination of the following outputs to a diskand/or display device, or a generating the output as a report to a user(e.g., a physician). The outputs discussed below may be usedindividually, collectively, or in any combination.

In one embodiment, an output may include a combination of the type andgrowth of disease, along with hemodynamic sensitivity. Such an outputmay be used to assess disease risk and treatment options. For example,patients with predicted fast growth of disease and high hemodynamicsensitivity to disease geometry may be at a higher risk of heartdisease. Patients with stable disease geometry may be at a lower risk.Patients with unstable disease geometry but low hemodynamic sensitivitymay be labeled as having indeterminate risk. Patients with indeterminaterisk may be candidates for frequent follow-ups or preventative medicaltherapy combined with close observations. An output may include apatient's risk level and one or more treatment recommendations based onthe risk level.

In another embodiment, an output may include a characteristic curve foreach identified disease region (e.g., from step 301 or method 320, step329). For example, a hemodynamics characteristic curve may show howhemodynamic quantities of interest vary with disease geometry. Forinstance, each curve may correspond to a type of disease, where thecurve may be plotted on a graph with geometry and a hemodynamic quantityof interest along the x-axis and y-axis.

In yet another embodiment, an output may include displaying one or morequantities of interest for a reconstructed geometry, as well as extremaof a family of geometries corresponding to disease progression anddisease regression (e.g., as described in more detail in FIG. 3C). Insome embodiments, the output may show quantities of interest for theextremes of disease progression/regression. In a further embodiment, anoutput may include a map of differences in hemodynamics, based onpredictive modeling of progression or regression of disease at desiredintervals, e.g., 3-month, 6-month, 1-year follow-up.

FIG. 3B is a block diagram of an exemplary method 320 of creating ananatomical model including diseased regions, according to an exemplaryembodiment. The method of FIG. 3B may be performed by server systems106, based on information, images, and data received from physicians 102and/or third party providers 104 over electronic network 101. Method 320may be an optional process, performed when an anatomical model and/orinformation regarding location of disease and disease type is notalready available.

In one embodiment, method 320 may begin with step 321 of acquiring, forinstance, a digital representation encompassing an anatomy or system ofinterest. The digital representation may include an image-basedrepresentation, measured variables, a list or table of parameter valuesand features representative of the system, or a combination of theabove. The digital representation may further be based on image scans,including CT, MRI, ultrasound, etc. saved using digital storage (e.g., ahard drive, network drive of a computer, laptop, server, or USB etc.).The representation acquired in step 321 may be of a specificindividual's anatomy, and therefore be considered patient-specific.

In one embodiment, next steps may include isolating diseased segments.For example, step 323 may include isolating a system of interest withinthe anatomy shown in the digital representation. For example, whenstudying coronary artery disease, a system of interest may include theaorta and relevant coronary arteries. Step 323 may include pinpointingan area or region of an individual's vasculature as a system ofinterest. Alternately or in addition, step 323 may include definingcharacteristics of a system of interest, so that the system of interestmay be identified within a set of information received regarding theindividual's vasculature. For example, step 323 of may includedelineating the geometry, specific conditions of the system of interest,and system properties. Specific conditions may include, for example, thediastolic phase at which an image was acquired. System properties mayinclude date and time of image acquisition and a patient condition atthe time of image acquisition, including blood pressure, height/weight,age, hematocrit/viscosity, etc. Patient conditions may vary over time,meaning, a single patient may undergo several patient conditions. Forinstance, a patient may be asymptomatic, then later experience symptomsof angina, then be post-acute myocardial infarction, emergencythrombolysis, and coronary stenting or CABG, or post-acute stroke andpost-operation amputation. In another scenario, a patient may have newlydiagnosed diabetes, cancer, stroke, dementia, and/or an aneurysm. In yetanother situation, a patient may be bedridden, for example, due totrauma, stroke, dementia, etc. System properties may include theproperties relating to and characterizing these various patient states.In some instances, step 323 may further include additional steps, forexample, steps for image processing.

In one embodiment, method 320 may include step 325 of determiningwhether to reconstruct a system and/or portions of a system from a rawimage (e.g., a raw image provided by step 321). Some embodiments mayinclude generating reconstructions, for example, where the acquiredrepresentation (e.g., from step 321) may display an incompleterepresentation, or where the acquired representation includes variablesor a list of parameter values, rather than an image-based model, orwhere image quality of the representation may be improved. From step325, method 320 may either follow step 327 of creating a reconstruction,or step 329 of defining diseased regions in the patient representation.

In some embodiments, step 329 may follow step 327, in which case step329 may include defining diseased regions in the reconstructed model(e.g., from step 327). In another embodiment, step 329 may includedefining diseased regions from the representation received (e.g., fromstep 321) using the geometry, system properties, and/or specificconditions delineated in step 323. One exemplary case may includeisolating and identifying stenoses where the system of interest includeslesions.

FIG. 3C is a block diagram of an exemplary method 340 of analyzing theimpact of disease progression and regression on hemodynamics, accordingto an exemplary embodiment. The method of FIG. 3C may be performed byserver systems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network101. In one embodiment, method 340 may include analyzing an impact thatdisease progression and/or regression may have on hemodynamics,especially hemodynamics related to the system of interest. For example,method 340 may include computing hemodynamics corresponding to patientgeometry (e.g., geometry available from step 301 or acquired and/orreconstructed from method 320 of FIG. 3B). In some cases, computing thehemodynamics may include solving Navier-Stokes equations to computeblood pressure and blood velocities, using boundary conditions specificto the anatomy and disease type. Exemplary methods for computinghemodynamics are disclosed, for example, in U.S. Nonprovisionalapplication Ser. No. 14/447,195, filed Jul. 30, 2014, entitled “Methodand System for Modeling Blood Flow with Boundary Conditions forOptimized Diagnostic Performance,” which is hereby incorporated byreference herein in its entirety. Alternatively or in addition,computing the hemodynamics may include using a reduced order model ormachine learning approach to compute hemodynamics.

In one embodiment, method 340 may include step 341 of identifying aproblem to study, as well as the progression/regression of plaque tostudy associated with that problem. For example, for coronary arterylesions, the amount of progression or regression for coronary arterylesions may be in the range of a fraction of milliliters, depending onthe size of a parent coronary artery. For another example, modeling softplaques may involve modeling a larger change in progression andregression of lesions. In some embodiments, the amount of progressionand regression of plaque may be in the form of percent change in acharacteristic dimension. In other words, plaque characteristics (e.g.,plaque vulnerability, composition, inflammatory status, etc.) may evolvein various disease conditions. By extension, progression and regressionof plaque may be represented by a percent change in, for instance,plaque vulnerability, composition, inflammatory status, etc. as adisease progresses. Various diseases have respective effects ondifferent areas in a vasculature. Thus, step 341 may include determiningor identifying an amount of progression/regression to be studied that isappropriate for the disease and conditions of interest, for a particularindividual.

In some embodiments, step 341 may include a threshold step of definingplaque progression and/or regression (e.g., prior to identifyingprogression/regression of plaque to study associated with a problem tostudy). Plaque composition may change over time. Also, progression andregression rates may vary over time, sometimes occurring simultaneouslyin different segments of one vessel, or in different vessels of a singlepatient. Furthermore, plaque may develop differently in various vascularbeds (e.g., cerebrovascular, coronary, aorta, peripheral vasculaturebeds) simultaneously in throughout the patient's body. Identifying theprogression/regression of plaque to study may include establishing athreshold definition for “plaque progression” versus “plaqueregression.”

Therefore, step 341 may include specifying a definition or set ofconditions that comprise “plaque progression” and a definition or set ofconditions that comprise “plaque regression.” For example, oneembodiment may include defining plaque progression as an increase inplaque volume. For such an embodiment, plaque regression may be definedas an enlargement of an artery in excess of plaque growth, resulting ina larger lumen. In other words, step 341 of defining plaque progressionand/or regression may mean identifying “plaque regression” where lumensize increases, even if plaque also expands in volume. Similarly, aplaque rupture (e.g., erosion of the fibrous cap) may be classified asplaque progression by some definitions of plaque progression. However,plaque rupture may result in an enlarged lumen, less obstruction toblood flow, a higher FFR_(CT). The situation involving an enlargedlumen, less obstruction to blood flow, and a higher FFRCT may beidentified as “plaque regression.” Finding the progression/regression ofplaque to study may include more than an analysis of anatomic features(e.g., size, volume, lumen caliber, and flow (e.g., geometricanatomic-functional model). Therefore, step 341 may include a step ofestablishing definitions for “plaque progression” and “plaqueregression” for the purposes of method 340.

In one embodiment, step 343 may include identifying or determining afamily of geometries of interest. The progression/regression of thedisease of interest may dictate the geometry to study. Then, step 343may further include defining a family of interest, based on the amountof progression/regression to study. A family of geometries of interestmay include a range of geometries and/or geometry characteristics. Forexample, a family of geometries may include a series of differentgeometries whose MLDs are near (e.g., +/−25%) the MLD of thereconstructed geometry, which may be sampled using the stochasticcollocation method.

In one embodiment, step 345 may include sampling the family ofgeometries (e.g., as determined in step 343) to calculate the impact ofdisease geometry on hemodynamics. In one embodiment, step 345 mayinclude sampling the family of geometries using a stochastic algorithm.In one embodiment, step 345 may further include a preliminary step ofselecting and/or determining a sampling method. For example, aMonte-Carlo algorithm may be a default method to perform the samplingfor step 345, where arbitrary geometries may be sampled from within thefamily of geometries uniformly. This process may depend on the number ofdisease regions (e.g., as identified by method 320 at FIG. 3B). Step 345may then include selecting an alternative sampling method.

For example, an alternative sampling for step 345 may include using astochastic collocation algorithm, where the geometries may be sampledbased on quadrature points of the Smolyak algorithm. Further, thesequadrature points may be adaptively sampled using the adaptive sparsegrid collocation algorithm based on how sensitive hemodynamics are tospecific diseases. An exemplary method for using an adaptive sparse gridalgorithm is disclosed, for example, in U.S. Nonprovisional applicationSer. No. 13/864,996, filed Apr. 17, 2013, entitled “Method and Systemfor Sensitivity Analysis in Modeling Blood Flow Characteristics,” whichis hereby incorporated herein by reference in its entirety. In oneembodiment, Navier-Stokes equations may be solved in each of the sampledgeometries to evaluate hemodynamics in response to changes in geometry.

In one embodiment, step 347 may include post-processing solved fluiddynamics equations, for instance, to compute a plot of the impact ofdisease on hemodynamics. In one embodiment, step 347 may includeoutputting the relationship between hemodynamics and disease geometry(e.g., to step 309). In a further embodiment, step 347 may includestoring the information. The information may then be retrieved for step309, wherein the information calculated in step 347 may be plotted andorganized or processed into renderings and/or reports.

An alternative to method 340 may include a machine learning approach,where the approach may iteratively calculate the impact of diseasegeometry on hemodynamics. Exemplary methods for machine learningapproaches are disclosed, for example, in U.S. Nonprovisionalapplication Ser. No. 14/011,151, filed Aug. 27, 2013, entitled “Systemsand Methods for Predicting Location, Onset, and/or Change of CoronaryLesions,” in U.S. Nonprovisional application Ser. No. 13/895,893 filedMay 16, 2013, U.S. Nonprovisional application Ser. No. 13/895,871 filedMay 16, 2013, and in U.S. Nonprovisional application Ser. No.13/864,996, filed Apr. 17, 2013, entitled “Method and System forSensitivity Analysis in Modeling Blood Flow Characteristics,” which arehereby incorporated by reference herein in its entirety.

FIG. 4A is a block diagram of an exemplary method 400 of a specificembodiment for treatment planning based on plaque progression/regressioncurves for artery disease (e.g., coronary artery disease), according toan exemplary embodiment. The method of FIG. 4A may be performed byserver systems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network101. In one embodiment, step 401 may include acquiring a digitalrepresentation of a system, for instance, a patient anatomy. In oneembodiment, the digital representation (e.g., the memory or digitalstorage (e.g., hard drive, network drive) of a computational device suchas a computer, laptop, DSP, server, etc.) may include an image scan ofan individual, including the ascending aorta and coronary artery tree.The image scan may include scans from cardiac computed tomography(CCTA), MRI, ultrasound, etc. The digital representation may furtherinclude a set of clinical parameters, including heart-rate, systolic anddiastolic brachial blood pressures, hematocrit, patient height andweight, patient history such as smoking status, presence/absence ofdiabetes, medicines used (e.g., antihypertensives, statins, etc.), etc.

In one embodiment, steps 403A and 403B may include preparing digitalrepresentations of the regions of interest. In some embodiments, steps403A and 403B may include determining that the received representation(e.g., from step 401) includes regions of interest. In anotherembodiment, step 403A and 403B may include reconstructing portions ofthe patient's anatomy before isolating one or more regions of interest.For example, step 403A may include a determination as to whether toreconstruct at least a portion of the patient anatomy. Ifreconstruction(s) are desired, step 403B may follow step 403A, in whichstep 403B may include performing the desired reconstruction. Forexample, step 403B may include computing centerlines, which pass throughthe centers of respective vessels of interest. Lumen segments may beconstructed manually or automatically to identify voxels belonging tothe aorta and to the lumen of the coronary arteries. Once all relevantvoxels are identified, step 403B may further include generating ageometric model of the aorta and relevant coronary arteries may bereconstructed.

In one embodiment, step 405 may follow either of steps 403A and 403B.Step 405 may include isolating diseased locations of the regions ofinterest. FIG. 4B includes a more detailed breakdown of exemplary stepsfor isolating diseased locations. In one embodiment, step 405 may employa basic threshold for merely detecting whether a location is diseased ornot diseased. Subsequent steps involve understanding the diseasedetected and its impact on an individual's health.

In one embodiment, step 407 may include characterizing the disease type.In other words, step 407 may involve profiling a disease at each of thediseased sites. For example, step 405 may yield a binary indication ofwhether or not a region is diseased. Step 407 may involve describingidentified disease at respective portions of the representation. In oneembodiment, characterizing disease type may include determining orrecognizing various classifications of disease. For example, type ofdisease may be broadly classified as lesions with a lipid rich core(soft plaque), collagen fibers (fibrotic), or hardened calcified plaque.Soft plaques may be first classified based on constitutive components,e.g., fraction of lipid, necrotic core, macrophages, collagen fibers,etc. Step 407 may include categorizing the diseased locations isolatedand identified in step 405, based on the determined and/or recognizedclassifications of disease. For example, classifications may be based onor similar to method 200 of FIG. 2.

Alternately or in addition, characterizing disease type in step 407 mayinclude classifying the disease at the diseased regions, e.g., bypropensity of plaque to remodel. In one embodiment, such classificationmay include using a remodeling index. Step 407 may further includecalculating such a remodeling index. For example, a remodeling index maybe estimated from the relationship between plaque progression and one ormore of: patient risk factors, medicines used by a patient, hemodynamicforces, and structural composition of the plaque. The remodeling indexmay be estimated using a machine learning approach by using follow-uppatient data, and mapping features of disease (e.g., composition,location, hemodynamic forces, etc.) to lesion remodeling.

Alternatively, the remodeling index may be estimated using a modelingapproach. For instance, a modeling approach may include computing aremodeling index by solving Navier-Stokes equations with appropriateboundary conditions to calculate the forces acting on plaque. Theseforces may be used along with structural properties of the plaque tofind homeostatic plaque configuration, for example, by solving stressequilibrium equations.

Step 409 may include calculating hemodynamic sensitivity of a lesion.One embodiment of step 409 may thus include calculating limits of plaquegeometry. For example, the plaque remodeling index computed in step 407may be used to calculate the limits of plaque geometry. In some cases,the limits of plaque geometry may include the maximum size that a plaquemay reach in a region, due to factors, e.g., before completely occludinga vessel, breaking off, destabilizing, etc. For instance, the plaqueremodeling index may quantify the limits of plaque geometry as arelationship between a (reference) vessel cross-sectional area and apoint of maximum stenosis. Maximal vessel stenosis may be measured fromthe luminal-intimal boundary to the outer vessel wall. A plaquecharacteristic curve may be estimated assuming a uniform variability ofthe plaque geometry within the limits of plaque geometry.

In one embodiment, analyzing hemodynamic sensitivity of a lesion mayinclude a stochastic collocation method. For example, step 409 mayinclude initializing stochastic collocation (quadrature) points usingthe Smolyak sparse grid algorithm, with each of the stochasticcollocation points corresponding to a unique plaque geometry. In somecases, the number of collocation points may depend on the collocation(quadrature) level. In addition, collocation levels may be nested, e.g.,with level 0 corresponding to one simulation and the number increasingwith levels. The actual number of collocation points may depend on howsensitive the hemodynamic solutions are to the disease geometry.

Then, for each identified geometry with a corresponding stochasticcollocation point, step 409 may include estimating FFR_(CT). Forexample, FFR_(CT) may be estimated either by solving Navier-Stokesequations using CFD methods, or by using a machine learning surrogatebased on patient-specific features. An exemplary method for estimatingFFR is disclosed, for example, in U.S. Nonprovisional application Ser.No. 13/864,996, filed Apr. 17, 2013, entitled “Method and System forSensitivity Analysis in Modeling Blood Flow Characteristics,” which ishereby incorporated by reference herein in its entirety. Step 409 mayinvolve identifying the hemodynamic sensitivity of a lesion bycalculating FFR_(CT).

In one embodiment, step 411 may include outputting analyses based on theplaque information in connection to hemodynamic sensitivity information.For example, step 411 may include outputting a patient risk map and/ortreatment options. In a further example, step 411 may include aselection of several displays for patient risk maps and/or treatmentoptions. Risk and treatment may be displayed in a variety of ways. Inone instance, a display may include a plot showing the hemodynamicsensitivity for each lesion identified. Disease type (e.g., from step407) may be included as annotations for each of the lesions, e.g., in adisplay created for a display device. The display may further beincluded as a report, e.g., for a physician. In one embodiment, thedisplay and/or report may also include a treatment option (e.g., atreatment option selected from a chart described in FIG. 2). Forexample, for an individual with slightly positive FFR_(CT) (e.g., 0.78)and plaques that are highly sensitive to plaque regression, lipidlowering agents (e.g., statin treatments) may be coupled to a treatmentoption, such as lifestyle modifications. Lifestyle modification mayinclude, for example, smoking cessation, control of dietary habits,frequent exercise (e.g., running), or managing stress levels. For anindividual having a negative FFR_(CT) (e.g., 0.85) and soft plaque thatis highly sensitive to progression, frequent follow-up monitoring may beconsidered. For FFR_(CT) positive patients with plaques sensitive toprogression, surgical alternatives such as stenting may be optimal. Insome embodiments, treatment planning (e.g., following method 200 in FIG.2) may be mapped to a risk score.

In another example, a display may include FFR_(CT) maps corresponding tothe extrema of plaque progression or regression (e.g., identified instep 409 and corresponding to the extrema of stochastic collocationpoints). Such maps may represent the worst state and best state ofhemodynamics within the range identified using a plaque remodelingindex. In yet another example, a display may include a sensitivity mapfor each lesion (e.g., as identified in step 409), where the sensitivityin the map may correspond to the relationship between FFR_(CT) andpositive and/or negative remodeling of the respective lesion. Typically,FFR_(CT) sensitivity may be highest in regions downstream of a plaque,but sensitivity may also be affected by regions proximal to a plaque ifrespective lesions are flow-limiting. This type of map may help identifythe relative impact of serial lesions, or help decide if stenting ishemodynamically beneficial. FIGS. 5-7 include exemplary displays, e.g.,FFR_(CT) maps, shown through visual representations on a diagram ormodel.

FIG. 4B is a block diagram of an exemplary method 420 for finding adiseased region, according to an exemplary embodiment. The method ofFIG. 4B may be performed by server systems 106, based on information,images, and data received from physicians 102 and/or third partyproviders 104 over electronic network 101. In one embodiment, step 421may include creating or receiving a definition of disease. Disease maybe defined with respect to geometry. For example, disease may be definedbased on lumen narrowing in coronary arteries. This may include definingdisease as a ratio of lumen radius to healthy lumen radius. Healthylumen radius may be calculated, in some cases, using maximum lumenradius in a coronary segment, average lumen radius in the coronarysegment, or by fitting a global radius curve for a coronary artery basedon patient-specific lumen radius (e.g., using Gaussian kernelregression). Step 421 may include defining a disease factor, forexample,

-   -   d=1−r/r_(healthy), if r<r_(healthy) and    -   d=0, if r<r_(healthy).

In one embodiment, step 423 may include determining a threshold diseasefactor, or disease factor range. The threshold disease factor or diseasefactor range may dictate whether a region of geometry is considereddiseased, for the purposes of method 400. In one embodiment, step 425may include applying patient-specific data to the definition (e.g., asspecified in step 421). In some instances, step 425 may includeretrieving geometry (e.g., “r” from the disease factor definition) fromthe geometric model of step 401. Then, the disease definition may beapplied to the retrieved geometry. In other words, step 425 may includecomputing a patient-specific disease factor. Step 427 may includecomparing the computed patient disease factor, to the threshold diseasefactor or disease factor range (e.g., from step 423). From thecomparison, step 429 may include determining whether disease exists at ageometric region at the location of input patient information. Forexample, d_(threshold) may be a value of 0.25, and coronary arterydisease may exist at locations where d>d_(threshold). This means step429 may designate regions of a patient geometry where a disease factoris computed to exceed 0.25, as diseased regions.

FIG. 4C is a block diagram of an exemplary method 440 for characterizinga disease type, according to an exemplary embodiment. The method of FIG.4C may be performed by server systems 106, based on information, images,and data received from physicians 102 and/or third party providers 104over electronic network 101. Method 440 may include characterizing adisease type in terms of the ability of plaque to remodel (e.g., plaquevolume growth potential and resulting lumen area coinciding with plaquegrowth).

In one embodiment, step 441 includes acquiring data, for instance,patient data. Patient data may include patient information including,for example, age, demographic, anatomy (e.g., digital representations),medical history, etc. Step 443 may include acquiring a single scan(e.g., a CT scan) of patient anatomy. This step may be similar to step401 of acquiring a digital representation of a patient anatomy. Step 445may include using plaque composition to predict growth potential of theplaque volume. In one embodiment, such a prediction may involve thesteps of method 200, including analyses of calcified plaque versus softplaque, as well as growth potential associated with the calcified versussoft plaque. Following this step, method 440 may proceed withcalculating hemodynamic sensitivity at diseased regions based on adisease type (e.g., a disease type based on the predicted growthpotential derived from plaque composition analysis) and outputting ananalysis based on hemodynamic sensitivity (steps 409 and 411,respectively).

Alternately or in addition, method 440 may include step 447 of acquiringand/or receiving multiple CT scans, if the scans are available. Forexample, the multiple CT scans may be associated with a patient atvarious states and/or times (e.g., patient baseline scan and follow-upscans). Alternately or in addition, the multiple CT scans may includescans of individuals other than the patient, e.g., scans of the otherindividuals at various states and/or times. In one embodiment, step 449may include registering locations of disease and delineating contours ofpatient (or individual) anatomy (e.g., contours of lumen, media,external elastic membrane (EEM), and internal elastic membrane (IEM)).In one embodiment, step 451 may include predicting growth of plaquevolume based on the locations of disease and contours of anatomy.Alternately or in addition, step 451 may include predicting lumen areausing the locations of disease and contours of anatomy from step 449.Upon determining plaque remodeling characteristics from steps 447, 449,and 451, method 440 may proceed with steps 409 and 441 (e.g.,calculating hemodynamic sensitivity at diseased regions based on adisease type and outputting an analysis based on hemodynamicsensitivity, respectively).

FIG. 5 shows an exemplary electronic display 500 including plaqueprogression/regression curves for a soft lipid-rich plaque, inaccordance with an embodiment of the present disclosure. Electronicdisplay 500 may be generated on any type of electronic device, such as acomputer screen, mobile screen, projection, etc., and on any displaytype, such as a software interface, website, etc. In one embodiment, oneoutput may include a plot 501 of plaque progression/regression curvesfor a soft lipid rich plaque, a location diagram 503, and a 3-Dpatient-specific model 505. For example, plot 501 may chart FFR againsta change in lesion radius (e.g., where change in lesion radius may beexpressed as a percentage change). In one case, the plot 501 may focuson how FFR evolves in response to various radii (or MLD) of plaque, forinstance in a range, where Δr=−25% to 25%. In one embodiment, diagram503 may depict lesion(s) and locations near the lesion(s) (e.g.,location A and location B).

In one embodiment, plot 501 may include progression/regression curve 507for the effect of a proximal lesion experienced at location B, curve 509for the effect of the proximal lesion experienced at location A, curve511 for the effect of a distal lesion experienced at location B, andcurve 513 for the effect, at location A, experienced as a result of thedistal lesion. Plot 501 may indicate that the severity of a lesion, theimpact of a lesion on blood flow, as well as a region where sensitivityis calculated as being relatively high, may be important in detectingtreatments related to plaque progression/regression. For example, curve507 may show that location B experiences little change in FFR inresponse to change in radius at the proximal lesion. Similarly, curve513 may indicate that, at location A, FFR experiences little change inFFR coming from the distal lesion. Curves 509 and 511, however, may showfar more hemodynamic sensitivity in response to plaque progression andregression at location A from the proximal lesion and at location B fromthe distal lesion, respectively.

Thus, curve 507 may show location B to be insensitive to statintreatment with respect to the proximal lesion and curve 513 may showlocation A to be insensitive to statin treatment with respect to thedistal region. Meanwhile, curve 509 may indicate that location A may besensitive to statin treatment with respect to the proximal lesion andcurve 511 may show location B to be sensitive to statin treatment at thedistal lesion. From the progression/regression curves, a user maydetermine, for instance, a target FFR level to reach, and determine orselect a treatment that may help a patient achieve the target FFR

In one embodiment, model 505 may include an option to scroll to betterpinpoint or view the locations in a 3-D view. Model 505 and diagram 503may further include options to select other locations, like locations Aand location B, and/or locations for lesions. Further, model 505 anddiagram 503 may show simulations of treatment application, for instance,a color-coded simulation illustrating blood flow at various locations,after statin treatment.

FIG. 6 shows an exemplary electronic display 600 including plaqueprogression/regression curves for a soft lipid-rich plaque, inaccordance with an embodiment of the present disclosure. Electronicdisplay 600 may be generated on any type of electronic device, such as acomputer screen, mobile screen, projection, etc., and on any displaytype, such as a software interface, website, etc. In one embodiment, anoutput may, again, include a plot 601, diagram 603, and 3-D model 605.The plot 601 may include a plaque regression/progression curve formultiple lesions. In the embodiment shown in plot 601, theregression/progression curves may be associated with serial lesions. Forexample, the serial lesions may be three serial lesions in the leftanterior descending (LAD) artery for a calcified plaque (e.g., shown atdiagram 603 as a distal lesion 607, middle lesion 609, and proximallesion 611). In other words, plot 601 may show the impact of threeplaques on FFR at various measurement locations. Model 605 may furthershow the LAD, e.g., in a 3-D view.

In one embodiment, diagram 603 and/or model 605 may include visualrepresentations of hemodynamic sensitivity and/or simulated blood flow,e.g., with color-coding or annotations. In one embodiment, diagram 603and/or model 605 may include options to change views (e.g., zoom in/zoomout functions, scrolling, rotation, etc.). Model 605 and diagram 603 mayfurther include options to select other locations to observe.

FIG. 7 shows an exemplary electronic display 700 of FFR_(CT) variationdue to remodeling of a lesion, in accordance with an embodiment of thepresent disclosure. Electronic display 700 may be generated on any typeof electronic device, such as a computer screen, mobile screen,projection, etc., and on any display type, such as a software interface,website, etc. In one embodiment, display 700 may include a model 701,along with various plots of FFR variation. Display 700 may be analternative way to show the relationship between FFR and plaque type andgeometry. In one embodiment, model 701 may show a lesion, e.g., throughcolor variation and/or annotations. Then, exemplary plots 703-711 mayshow FFR_(CT) variation at various places near and along the lesion,from an upstream location to downstream locations. In one embodiment,model 701 may depict change in FFR_(CT) by color (or another visualrepresentation), and plots 703-711 may contain more detail on the trendin FFR_(CT) variation at each location. In one embodiment, a user mayinteract with model 701. For example, a user may scroll along the lesionshown, where plots may adjust according to the location of thescrolling. As with diagrams and models from display 500 and display 600,display 700 may permit user selection and various functions or optionsin views of the model 701. In the example shown for FIG. 7, FFR_(CT)downstream of a lesion may increase due to reduced pressure drop whenplaque positively remodels. Also, FFR_(CT) upstream may experience aslight drop due to increased flow rate immediately upstream of a lesion.

This disclosure may additionally apply to computing hemodynamicquantities of interest, beyond FFR in the coronary arteries. Forexample, in additional or alternate embodiments, the sensitivity ofblood flow rate, tissue perfusion, pressure, shear stress, plaque force,or rupture risk on plaque progression or regression may be assessed.

Alternate embodiments of the invention may apply to quantifying theeffect of progression or regression of atherosclerotic plaques in thecarotid artery, intracranial cerebral arteries, superficial femoralartery and renal artery, etc. In each of the cases, this invention maybe used to quantify the effect of plaque geometry changes on relevanthemodynamic quantities, including blood flow rate, blood pressure, ortissue perfusion.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-20. (canceled)
 21. A method for non-invasive assessment and therapyplanning for coronary artery disease from medical image data of apatient, comprising: extracting geometric features from medical imagedata representing a geometry of the patient; detecting one or morelesions in the geometry of the patient; determining a plurality ofpressures at one or more points of interest along the geometry of thepatient including multiple points within each of the one or more lesionsbased on the extracted geometric features using a first machine learningmodel; predicting post-treatment values for the plurality of pressuresat the one or more points of interest along the geometry of the patientincluding the multiple points within each of the one or more lesions foreach of one or more candidate treatment options for the patient; anddisplaying a visualization of a treatment prediction for at least one ofthe candidate treatment options for the patient.
 22. The method of claim21, wherein the first machine learning model comprises a plurality ofmachine learning algorithms, and determining the plurality of pressuresat the one or more points of interest along the geometry of the patientincluding multiple points within each of the one or more lesions basedon the extracted geometric features using a first machine learningalgorithm of the plurality of machine learning algorithms comprises:determining the plurality of pressures at points along healthy segmentsof the geometry of the patient using the plurality of machine learningalgorithms; and determining the plurality of pressures at the multiplepoints within each of the one or more lesions using the plurality ofmachine learning algorithms.
 23. The method of claim 22, whereindetermining the plurality of pressures at the multiple points withineach of the one or more lesions using the plurality of machine learningalgorithms comprises, for each of the one or more lesions: receiving aplurality of feature vectors of a patient's physiological parametersassociated with a plurality of blood flow characteristics; and computinga plurality of predicted blood flow characteristics of the one or morelesions based on the plurality of feature vectors associated with theplurality of blood flow characteristics.
 24. The method of claim 21,wherein the first machine learning model comprises a support vectormachine (SVM).
 25. The method of claim 21, wherein the first machinelearning model comprises a multi-layer perceptron (MLP).
 26. The methodof claim 21, wherein the first machine learning model comprises amultivariate regression (MVR).
 27. The method of claim 21, wherein eachof the one or more candidate treatment options corresponds to one ormore candidate percutaneous coronary intervention (PCI) treatments, andpredicting post-treatment values for the plurality of pressures at theone or more points of interest along the geometry of the patientincluding the multiple points within each of the one or more lesions foreach of one or more candidate treatment options for the patient,comprises: inputting the extracted geometric features to a secondtrained machine learning model; predicting patient-specific post-PCIgeometric features for each of the one or more candidate PCI treatmentsbased on the inputted extracted geometric features using the secondtrained machine learning model; and determining, for each of the one ormore candidate PCI treatments, post-PCI values for the plurality ofpressures at the one or more points of interest along the geometry ofthe patient including the multiple points within each of the one or morelesions based on the predicted patient-specific post-PCI geometricfeatures using the first machine learning model.
 28. The method of claim21, wherein each of the one or more candidate treatment optionscorresponds to a candidate percutaneous coronary intervention (PCI)treatment, and determining the plurality of pressures at the one or morepoints of interest along the geometry of the patient including themultiple points within each of the one or more lesions for each of oneor more candidate treatment options for the patient, comprises:inputting the extracted geometric features, a number of the lesionsdetected in the geometry of the patient, and locations of the lesionsdetected in the geometry of the patient to a second trained machinelearning model; and determining, for each of one or more candidate PCItreatments corresponding to respective possible combinations of stentingat the lesions in the geometry, post-PCI values for the plurality ofpressures at the one or more points of interest along the geometry ofthe patient including the multiple points within each of the one or morelesions based on the input extracted geometric features using the secondtrained machine learning model.
 29. The method of claim 21, wherein eachof the one or more candidate treatment options corresponds to acandidate percutaneous coronary intervention (PCI) treatment, and themethod further comprises: determining, for each of the one or morecandidate PCI treatments, a plaque remodeling index using a secondtrained machine learning model based on the geometric featurescorresponding to post-PCI anatomy for each of the one or more candidatePCI treatments and other features including one or more of demographicfeatures or blood biomarkers.
 30. The method of claim 29, whereinpredicting, for each of the one or more candidate PCI treatments, aplaque remodeling index using a second trained machine learning modelbased on the geometric features corresponding to post-PCI anatomy foreach of the one or more candidate PCI treatments and other featuresincluding one or more of demographic features or blood biomarkerscomprises: determining, for each of the one or more candidate PCItreatments, the plaque remodeling index at the plurality of points alongthe geometry including the multiple points within each of the one ormore lesions using the second trained machine learning model, resultingin a respective sensitivity curve for each of the one or more candidatePCI treatments, wherein the plaque remodeling index at each pointcorresponds to a likelihood that the one or more lesions arefunctionally significant.
 31. The method of claim 30, wherein each ofthe one or more candidate PCI treatments includes one or more stentinglocations, and displaying a visualization of a treatment prediction forat least one of the candidate treatment options for the patientcomprises: displaying, for at least one of the candidate PCI treatments,an image showing at least a portion of the geometry of the patient witha visual representation of a treatment option overlaid on the geometryat the one or more stenting locations for the candidate PCI treatment,the respective sensitivity curve for the candidate PCI treatments, andthe determined plurality of pressures for the candidate PCI treatments.32. The method of claim 29, further comprising: outputting a risk scorefor the one or more candidate PCI treatments based on the predictedpost-treatment values of the determined plurality of pressures and thedetermined plaque remodeling index for each of the one or more candidatePCI treatments.
 33. A system for non-invasive assessment and therapyplanning for coronary artery disease from medical image data of apatient, comprising: at least one data storage device storinginstructions for controlling image segmentation; and at least oneprocessor configured to execute the instruction to perform operationscomprising: extracting geometric features from medical image datarepresenting a geometry of the patient; detecting one or more lesions inthe geometry of the patient; determining a plurality of pressures at oneor more points of interest along the geometry of the patient includingmultiple points within each of the one or more lesions based on theextracted geometric features using a first machine learning model;predicting post-treatment values for the plurality of pressures at theone or more points of interest along the geometry of the patientincluding the multiple points within each of the one or more lesions foreach of one or more candidate treatment options for the patient; anddisplaying a visualization of a treatment prediction for at least one ofthe candidate treatment options for the patient.
 34. The system of claim33, wherein the processor is further configured to determine, for eachof one or more candidate percutaneous coronary intervention (PCI)treatments, a plaque remodeling index using a second trained machinelearning model based on the geometric features corresponding to post-PCIanatomy for each of the one or more candidate PCI treatments and otherfeatures including one or more of demographic features or bloodbiomarkers.
 35. The system of claim 34, wherein the processor is furtherconfigured to output a risk score for the one or more candidate PCItreatments based on the predicted post-treatment values of thedetermined plurality of pressures and the determined plaque remodelingindex for each of the one or more candidate PCI treatments.
 36. Thesystem of claim 33, the operations further comprising: inputting theextracted geometric features, a number of the lesions detected in thegeometry of the patient, and locations of the lesions detected in thegeometry of the patient to a second trained machine learning model; anddetermining, for each of one or more candidate percutaneous coronaryintervention (PCI) treatments corresponding to respective possiblecombinations of stenting at the detected lesions in the geometry,post-PCI values for the plurality of pressures at the one or more pointsof interest along the geometry of the patient including the multiplepoints within each of the one or more lesions based on the inputextracted geometric features using the second trained machine learningmodel.
 37. A non-transitory computer readable medium for use on acomputer system containing computer-executable programming instructionsfor non-invasive assessment and therapy planning for coronary arterydisease from medical image data of a patient, the instructions beingexecutable by the computer system for: extracting geometric featuresfrom medical image data representing a geometry of the patient;detecting one or more lesions in the geometry of the patient;determining a plurality of pressures at one or more points of interestalong the geometry of the patient including multiple points within eachof the one or more lesions based on the extracted geometric featuresusing a first machine learning model; predicting post-treatment valuesfor the plurality of pressures at the one or more points of interestalong the geometry of the patient including the multiple points withineach of the one or more lesions for each of one or more candidatetreatment options for the patient; and displaying a visualization of atreatment prediction for at least one of the candidate treatment optionsfor the patient.
 38. The non-transitory computer readable medium ofclaim 37, the instructions further comprising: determining, for each ofone or more percutaneous coronary intervention (PCI) candidatetreatments, a plaque remodeling index using a second trained machinelearning model based on the geometric features corresponding to post-PCIanatomy for each of the one or more candidate PCI treatments and otherfeatures including one or more of demographic features or bloodbiomarkers.
 39. The non-transitory computer readable medium of claim 38,the instructions further comprising: outputting a risk score for the oneor more candidate PCI treatments based on the predicted post-treatmentvalues of the determined plurality of pressures and the determinedplaque remodeling index for each of the one or more candidate PCItreatments.
 40. The non-transitory computer readable medium of claim 37,the instructions further comprising: inputting the extracted geometricfeatures, a number of the lesions detected in the geometry of thepatient, and locations of the lesions detected in the geometry of thepatient to a second trained machine learning model; and determining, foreach of one or more candidate percutaneous coronary intervention (PCI)treatments corresponding to respective possible combinations of stentingat the detected lesions in the geometry, post-PCI values for theplurality of pressures at the one or more points of interest along thegeometry of the patient including the multiple points within each of theone or more lesions based on the input extracted geometric featuresusing the second trained machine learning model.